Synthesis of magnetic metal-organic framework (MOF) for efficient removal of organic dyes from water
Bibliographic record
Abstract
A novel, simple and efficient strategy for fabricating a magnetic metal-organic framework (MOF) as sorbent to remove organic compounds from simulated water samples is presented and tested for removal of methylene blue (MB) as an example. The novel adsorbents combine advantages of MOFs and magnetic nanoparticles and possess large capacity, low cost, rapid removal and easy separation of the solid phase, which makes it an excellent sorbent for treatment of wastewaters. The resulting magnetic MOFs composites (also known as MFCs) have large surface areas (79.52 m(2) g(-1)), excellent magnetic response (14.89 emu g(-1)), and large mesopore volume (0.09 cm(3) g(-1)), as well as good chemical inertness and mechanical stability. Adsorption was not drastically affected by pH, suggesting π-π stacking interaction and/or hydrophobic interactions between MB and MFCs. Kinetic parameters followed pseudo-second-order kinetics and adsorption was described by the Freundlich isotherm. Adsorption capacity was 84 mg MB g(-1) at an initial MB concentration of 30 mg L(-1), which increased to 245 mg g(-1) when the initial MB concentration was 300 mg L(-1). This capacity was much greater than most other adsorbents reported in the literature. In addition, MFC adsorbents possess excellent reusability, being effective after at least five consecutive cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".